[Bugfix] fix bug of pcp+mtp+async scheduler (#5994)

### What this PR does / why we need it?
Fixed the issue where the PCP and MTP services could not be started due
to asynchronous scheduling.

After the pcp, mtp, and asynchronous scheduling functions are enabled,
the service is suspended because of a shape mismatch after a curl
request is sent. This PR resolves this issue.

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
2c24bc6996

---------

Signed-off-by: weiguihua2 <weiguihua2@huawei.com>

vllm_ascend/worker/model_runner_v1.py

@@ -226,6 +226,7 @@ class NPUModelRunner(GPUModelRunner):
                 self.max_num_reqs,
                 self.device,
                 self.vllm_config,
+                self.use_async_scheduling,
                 self.pin_memory,
             )
             # TODO(zhenwenqi) after https://github.com/vllm-project/vllm/pull/28988 is merged, we can delete this
@@ -540,10 +541,18 @@ class NPUModelRunner(GPUModelRunner):
         # for pcp, prefill mtp should use origin scheduleroutput ,
         if self.speculative_config and self.pcp_size * self.dcp_size > 1:
             self.pcp_manager.generate_pcp_mtp_input(
-                num_reqs, total_num_scheduled_tokens,
-                scheduler_output.num_scheduled_tokens, with_prefill,
-                self.input_batch, self.arange_np, req_indices, positions_np,
-                cu_num_tokens)
+                num_reqs,
+                total_num_scheduled_tokens,
+                scheduler_output.num_scheduled_tokens,
+                with_prefill,
+                self.input_batch,
+                self.arange_np,
+                req_indices,
+                positions_np,
+                cu_num_tokens,
+                self._draft_token_ids,  # type: ignore[has-type]
+                scheduler_output,
+                self.num_spec_tokens)
 
         if self.pcp_size > 1:
             if not self.vllm_config.model_config.use_mla:
@@ -929,7 +938,7 @@ class NPUModelRunner(GPUModelRunner):
             if self.pcp_size * self.dcp_size > 1:
                 self.long_seq_metadata = self.pcp_manager.generate_pcp_metadata(
                     total_num_scheduled_tokens, self.query_lens,
-                    self.input_batch)
+                    self.input_batch, num_scheduled_tokens)
                 blk_table.slot_mapping.gpu[maybe_pcp_full_tokens:].fill_(-1)
                 if self.pcp_size > 1:
                     slot_mapping_pcp = self.pcp_manager.get_padded_slot_mapping(
@@ -1946,7 +1955,8 @@ class NPUModelRunner(GPUModelRunner):
                 slot_mapping = self.input_batch.block_table[
                     kv_cache_group_id].slot_mapping
                 long_seq_metadata = None if self.pcp_size * self.dcp_size == 1 else self.pcp_manager.generate_pcp_metadata(
-                    num_tokens, self.query_lens, self.input_batch)
+                    num_tokens, self.query_lens, self.input_batch,
+                    num_scheduled_tokens)
                 if long_seq_metadata is not None:
                     pcp_world_size = get_pcp_group().world_size
                     dcp_world_size = get_dcp_group().world_size